{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import sklearn\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LinearRegression \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取训练集和测试集的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traindata = pd.read_csv('./Ames_House_train.csv')\n",
    "testdata = pd.read_csv('./Ames_House_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# 了解数据具体信息\n",
    "traindata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 了解数据前两行信息\n",
    "traindata.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453</td>\n",
       "      <td>0.995205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406</td>\n",
       "      <td>0.963014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369</td>\n",
       "      <td>0.937671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179</td>\n",
       "      <td>0.807534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "      <td>0.472603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "      <td>0.177397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total   percent\n",
       "PoolQC         1453  0.995205\n",
       "MiscFeature    1406  0.963014\n",
       "Alley          1369  0.937671\n",
       "Fence          1179  0.807534\n",
       "FireplaceQu     690  0.472603\n",
       "LotFrontage     259  0.177397\n",
       "GarageCond       81  0.055479\n",
       "GarageType       81  0.055479\n",
       "GarageYrBlt      81  0.055479\n",
       "GarageFinish     81  0.055479\n",
       "GarageQual       81  0.055479\n",
       "BsmtExposure     38  0.026027\n",
       "BsmtFinType2     38  0.026027\n",
       "BsmtFinType1     37  0.025342\n",
       "BsmtCond         37  0.025342\n",
       "BsmtQual         37  0.025342\n",
       "MasVnrArea        8  0.005479\n",
       "MasVnrType        8  0.005479\n",
       "Electrical        1  0.000685\n",
       "Utilities         0  0.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = traindata.isnull().sum().sort_values(ascending=False)\n",
    "percent = (traindata.isnull().sum()/traindata.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total,percent],axis=1,keys=['total','percent'])\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 缺失值的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traindata = traindata.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)\n",
    "testdata = testdata.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用corr()计算出各列与'SalePrice'的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 选出只有整数和浮点数的数据\n",
    "num_traindata = traindata.select_dtypes(include = ['int64','float64'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below are 37 correlated values with SalePrice:\n",
      "OverallQual      0.790982\n",
      "GrLivArea        0.708624\n",
      "GarageCars       0.640409\n",
      "GarageArea       0.623431\n",
      "TotalBsmtSF      0.613581\n",
      "1stFlrSF         0.605852\n",
      "FullBath         0.560664\n",
      "TotRmsAbvGrd     0.533723\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "GarageYrBlt      0.486362\n",
      "MasVnrArea       0.477493\n",
      "Fireplaces       0.466929\n",
      "BsmtFinSF1       0.386420\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "2ndFlrSF         0.319334\n",
      "OpenPorchSF      0.315856\n",
      "HalfBath         0.284108\n",
      "LotArea          0.263843\n",
      "BsmtFullBath     0.227122\n",
      "BsmtUnfSF        0.214479\n",
      "BedroomAbvGr     0.168213\n",
      "ScreenPorch      0.111447\n",
      "PoolArea         0.092404\n",
      "MoSold           0.046432\n",
      "3SsnPorch        0.044584\n",
      "BsmtFinSF2      -0.011378\n",
      "BsmtHalfBath    -0.016844\n",
      "MiscVal         -0.021190\n",
      "Id              -0.021917\n",
      "LowQualFinSF    -0.025606\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "MSSubClass      -0.084284\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 用corr()计算出各列与'SalePrice'的相关性\n",
    "traindata_corr = num_traindata.corr()['SalePrice'][:-1]\n",
    "golden_feature_list = traindata_corr[abs(traindata_corr) > 0].sort_values(ascending = False)\n",
    "print(\"Below are {} correlated values with SalePrice:\\n{}\".format(len(golden_feature_list), golden_feature_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdafa048>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O6GcmFaXr/b1xaliLzF1Hy9a7He6APuy/o4UtKHWpF62PBB63Lg8E1lr/7wG8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\nedAqrqDD+6PY+9Qc8uLT6PfrLOIqMIY3GzuA1BNlFvLgwV3ROTmyve9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Way+5r0ef9gTP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\nB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vBfEfm3qu4TkUpAKaAsRrH9kIhEA30wTjtTRCQKowwRWKreDLQDZgJXZWM8GarEe9Gk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\n57AdDoejgPD/a4PwIPfRyFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd875940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traindata_corrheatmap = num_traindata.corr()\n",
    "cols = traindata_corrheatmap.nlargest(10, 'SalePrice')['SalePrice'].index\n",
    "cm = np.corrcoef(num_traindata[cols].values.T)\n",
    "sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', \n",
    "            annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看'SalePrice'的分布情况\n",
    "traindata['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdd9d1d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iWMzRR0dX29e+Fhd/y69DQ5elkqjjJQP33w9f/jIsXx4H7D/4wawjkkpzwAHw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\niJSVEouIiJSVEouIiJTV/wCF4YlVg2ed6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdac3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#创建一个直方图来看一下数据分布状况。\n",
    "sns.distplot(traindata['SalePrice'],color='b',bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B+CYwCDgt2jfbOUREWpRKQffu8RJN0QydrVwZrsMcdBB8+ctwww1w+OHw0EMwdy787GdF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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde4ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probmap = stats.probplot(traindata['SalePrice'],plot = plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe31db38>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K+JZbPt+2e/eY5dWnT8y62Wqr2C0s//KaNTE02dIVnQppZq2B14ADgIXANGCM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\nRCSDlNxFRDJIyV1EJIOU3EVEMuj/Ay6F2FT9Q16QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3d5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(traindata['SalePrice']),color='b',bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fHmQcvfgr/ZnM+mzNC/yGmzl39DquUzQgJwwzK5uBA5NEUahV0ZNXGcMuPMjuLMP37Msd\n/JSneYmtGTDAI58ammsYZtagsiwSuAbTOI8zOIxRfEEHBnEF1zCA2SQF7Z12SrbVtoblFoaZNYjK\nytpbEwDLM5OzGMrbdKMvlVzGH1mfyVzJIGbTBglGj4ZHHmm4uG0BtzDMrOSKrSjbirkczt85lzNZ\ng/9xCwdwOsN5j3Xnn7PTTk4U5eaEYWYlVawLahce4hJOZBPG8yzbsg938SLbzH/diaLxcJeUmZVM\nbbO0N+E1/s2uPEQf2jKL/bid7XnGyaIRcwvDzEqmpm6oNZjGuZzJ4fydGbTneC7jagbyA8sA0L49\nfPFFAwdqmbiFYWb1LjevIl9bvmEoZ/E23TiYG7mc41mfyVzB8fOTxYABThaNmVsYZlavWreGefMW\nPG/FXA5jFOdyJp34iNvYn9M4n3dZLxn1dKPnUzQVThhmVi9qWihwZ8ZwCSfSk9d5nm3Yj3/yPNsC\nsNxyMGtWGQK1xVanLilJHST1LFUwZta05GZqV1/WowfjeZA+jGFX2jGT/bmNbXlufrJo397Joikq\n2sKQ9ASwV3ruOGC6pCcj4o8ljs3MGrE2bWD27IWP/YiPOIchHMFIvmJF/sil/IVj5tcoclynaJqy\ntDBWioivgH2Bv0fEFkDvYhdJGinpE0nj845dLOkNSa9JuktSjUtMSnpf0uuSxkmqyvrDmFnp5WZs\n5yeLtnzDmZzD23TjUG7gSo5jPd7hcv64SLIYPbqBA7Z6kyVhLCVpDeDXwH11uPcooE+1Yw8DG0dE\nT+At4LRart8xInpFREUd3tPMSqhDB+jXb8HzZIb2SN6mG+cwlH/Th+5M5I9czhesvNC1uWU9XOBu\nurIkjHOAh4B3IuJlSesCbxe7KCKeAj6vdmxMRMxJn74ArFXHeM2sDHK1ihkzFhzrzcO8wuaM5Ej+\nS2e24xn255+8w/qLXD9gQDJyysmiaStaw4iI24Hb856/C/xfPbz3EcCthd4WGCMpgOsiYkShm0jq\nD/QH6Ny5cz2EZWb5qtcqujOBizmJ3XmQ9+jKAdzCbfwa0CLXeiRU81K0hSFpA0mP5moRknpKOmNJ\n3lTSYGAOUFnglO0iYnNgN+AYST8rdK+IGBERFRFR0bFjxyUJy8zydOiwcK1idf7HtfyO1+jJtjzH\nCVzCj3mD2ziAmpLFgAFOFs1Nli6pv5LUGmYDRMRrwIGL+4aSDgV+CfSNiKjpnIiYln7/BLgL2Gpx\n38/M6i6/+2k5ZnEG5zKZ9TmCkfyZ37Me73AZJyxS0O7eHSKSL+9X0fxkmbjXNiJe0sLz/OcUOrk2\nkvoApwA7RESNf3tIWh5oFRFfp493IamjmFkDyP1Tb8VcDuZGhjGYNZnGHezLqVzAZLrVeF3Nf/5Z\nc5KlhfGppPVI6gpI2g/4qNhFkm4Gngc2lDRV0pHAVcAKwMPpkNlr03M7SXogvXR14BlJrwIvAfdH\nxL/r+oOZWXb5E/AAfsGjVFHBKA5nKmuxPU+zH3fUmCyWW87JoqXI0sI4BhgB/FjSh8B7QL/aL4GI\nOKiGw38rcO40YPf08bvAphniMrMlVFm58DDZ7kzgIk5mDx7gfbpwIDdzG78mCvxtOWCAu55akiyj\npN4Feud3FZU+LDMrtfy1n1bjY85mKL/lr3zNCpzERfyZ3/M9y9Z4rZcgb5myLA0ypNpzACLCdQWz\nJih/B7zlmMXxXM6pXMCyfMdfOIZzGMJnrFrwek++a7mydEl9k/d4WZIRTpNKE46ZlUp+ohDz6Mdo\nhjGYtZnKnezDqVzA22xQ8Hq3KixLl9Sl+c8lXQLcW7KIzKzetW0L336bPN6Rx7iUE9iMcbzElvSl\nkqcpONUJgE6d4MMPGyBQa9QWZ8e9tsC69R2ImZWGlCSLHzOJe9mTx9iJDnzBQdzENrxQNFmMHu1k\nYYksNYzXSYfUAq2BjnhehFmjlxsi25FP5he0v2F5TuZCruS4ggVtcPeT1SxLDeOXeY/nAB/nLSBo\nZo2QBMvy7fyCdltmcQ0DOIchfErtS+h4ToUVUjBhSMqtTVx9GO2KkoiIz6tfY2bl1bYtfPftPPpR\nyXBOZ22mcjd7cwoX8hYb1nqtWxVWTG0tjLEkXVGLriqWHHcdw6wRkWAHnuBSTmALXqGKLejHaJ5i\nh1qvc6KwrAomjIhYpyEDMbPFM3AgPHbNG9zDyezFv/gva9OX0dzMQQVnaOe4+8nqIksNA0kdgG6w\noEqWbpBkZmW0mj5hKGdzJdcxi7acyvn8iUF8x3K1XudWhS2OLKOkjgIGkeyONw7YhmRRwV+UNjQz\nK2S9Tt+y/0d/YjLDacssruVozmZo0YI2eKa2Lb4sLYxBwJbACxGxo6QfA2eXNiwzq9G8efRrfROP\nczqd+YB72ItTuJA3+XHRS50obEllmbj3XUR8ByBpmYh4A4oMtzCzetW7N+ygJ6lqvRWjOZjpdGRH\nHuNX3ONkYQ0mSwtjqqT2wN0k+1h8AUwrbVhmBklB+9Fr3uQiTmZv7uW/rE0/buQmflO0oA3JDngT\nJjRAoNYiZFlLap/04VmSHgdWAryhkVmJddT0tKB9LbNoy2kM5wr+ULSgneNWhdW32ibu3Q/cBNwd\nEd8ARMSTDRWYWUvUoQN8O+M7BpEUtJfnG0bQn7M4i+mslvk+Hi5rpVBbm3YEybIg70u6VdKvJLVp\noLjMWpQePaCV5rHbjJt4kw25kFN5kh3YhNc5hqszJ4vRo50srHRqm7h3D3CPpOWAvYBDgWvTvbdv\njoiHGyhGs2Yrt0DgT3mKFziRrXiZV9iMw/k7j9dh5LrnVVhDKFo1i4hvI+LWtJaxC7AZrmGYLbaB\nA5NEIUE33uJO9uEpdqAT0ziEG6igqk7JIsLJwhpGlol7qwO/Bg4E1gBuBw4vcVxmzVKuRbEKnzKU\nszmaa/mOZRnMeVzO8XxL20z3WXpp+OGHEgZqVoOCLQxJv5X0GPAKsAFwckSsGxGnRMS4BovQrBnI\ntSqW4TtO4iLeYT0GcjXXcxTrM5nhDM6cLCKcLKw8amthbAtcADwSEfMaKB6zZiW3j7aYx4Hcyvmc\nRlemcB97cDIXMYnume/lYraVW8EWRkQcHhFjFjdZSBop6RNJ4/OOXSzpDUmvSbornRBY07V9JL0p\nabKkUxfn/c3KrXXrJFlsz9O8wDbczG+YQXt24hH25L5MyWK55ZJE4WRhjcHi7Omd1SigT7VjDwMb\nR0RP4C3gtOoXSWoN/AXYDegOHCQp+59hZmXWtm3S/bTuvLe5g315mp/RiWkcyii2YCyPsVPReyy9\ndJIkZs1qgIDNMipZwkiXP/+82rExedu7vkCyAm51WwGTI+LdiPgBuAXYu1RxmtUnCZb79jOuYBAT\n6c4ujOEMzmUD3uIfHMo8Wtd6fa414RqFNUa1Fb1Xru2rHt77CODBGo6vCXyQ93xqeqxQnP0lVUmq\nmj59ej2EZVY3vXune2jrO07gEt5hPY7lKkZyBOszmWGcUbSg7W4nawpqa2GMBarS79NJupDeTh+P\nXZI3lTQYmANU1vRyDccK/lOKiBERURERFR07Ft8LwKw+SfDoo8EB3MIkNuISTuI5tqUnr3E01/Ex\nP6r1+gEDnCis6Si6Rauka4F7I+KB9PluQO/FfUNJh5IsObJTRI3/VKYCa+c9XwuvjmuNzJprwrRp\nsC3PciknsA0v8io92ZkxPMLOme7hRGFNTZYaxpa5ZAEQEQ9CkV3lC5DUBzgF2CsiCpXzXga6SVon\nXbvqQODexXk/s/rWoUNap5g2mdvZj2fZnrX5gMMZyea8kilZuPvJmqosCeNTSWdI6iqpS9qd9Fmx\niyTdTLKV64aSpko6ErgKWIFkX41xaesFSZ3SNapIi+LHAg8Bk4DbIsIr+ltZrblmkihazfiMy/kD\nE+lOH/7NEM5mA95iFIcXLWjnhsiaNVWquVco74SkwD0U+BlJLeEp4JyI+LzWC8ugoqIiqqqqyh2G\nNRMDB8I11ySP2/A9x3IVZ3AeK/IVf+NIhnI2/2ONovfxwoDWmEkaGxEVWc7NsoHS58AgSe0iYuYS\nR2fWBHToADNmAAT7czsXcCrr8h4P0oeTuJgJbJzpPm5RWHNStEtK0raSJgIT0+ebSrq65JGZNbDc\nCrJSkix+wnM8x7bcxgHMpB278BC782CmZOE6hTVHWWoYlwO7ktYtIuJVku4psyavR48FSSJnXd7h\nNvbnObajC1M4gr+xGf/hYXYper/27Z0orPkq2iUFEBEfSAtNj5hbmnDMSi83JLa6DnzOGZzHsVzF\nbJZmKGdxKSfwDe2K3nO55byMhzV/WRLGB5K2BSId5nocyeglsyalRw+YOHHR4234noFczZmcS3tm\nMJIjOJNzXdA2qyZLwjga+BPJ8hxTgTHAMaUMyqw+qaa1AwAI9uOfXMCprMe7PMQunMTFvE7Povd0\ni8JaoloTRrpy7MER0beB4jGrFwtGOdVsG57nUk5gW57ndTZmV/7NGHYtel/XJ6wlq7XoHRFz8Uqx\n1oTklhYvlCzW4V1u4QCeZ1vW4T2O4q/0YlzRZJFbbtysJcvSJfWspKuAW4Fvcgcj4pWSRWVWB4WK\n2Pna8wVncB6/58/MZmnOYiiXcGKtBW0nCLOFZUkY26bfz8k7FsAv6j8cs+wqK6Ffv9rPWZofGMjV\nDOEc2jODv3M4QziHaQVWzHdtwqywLDO9d2yIQMzqoniyCPblTi7kFNbnHcawMydxMa+x6SJneqST\nWTZZZnqvLulvkh5Mn3dPFxI0a1D5k+xqSxZb8SJP81PuYD++Y1n68CC78tAiySI3yc7JwiybLDO9\nR5GsHNspff4W8IdSBWRW3cCBSZKoaQ5Fvq68x80cyItsw/pM5reMoBfjeIg+5O/LlVu2w4nCrG6y\nJIxVI+I2YB7MX37cM72t5HJbn+ZWjC2kPV9wESfxBj9mL+7lHM6kG29zPb9lbtrr2r2713cyW1JZ\nit7fSFqFdJtUSdsAX5Y0Kmvx2rSB2bNrP2dpfmAA1zCEc+jAF4ziMM7k3IUK2gMGwNVeKtOsXmRp\nYfyRZMe79SQ9C/wD+H1Jo7IWq7IyaVXUniyCfbiTCfTgT/yB/7AZm/MKRzKSaazJTjstaE04WZjV\nnyyjpF6RtAOwIUlH8JsRUeRvP7PsssyjyNmSl7iUE/gpzzCB7uzO/TzIbuRqFKNHQ1+vS2BWEgUT\nhqR9C7y0gSQi4s4SxWQtRF0SRRfe53xO4yBu4WNWoz/XMZIj5tcowMnCrNRqa2HsmX5fjWTy3mPp\n8x2BJwAnDKuzLJPt8q3EDE5nOIP4E3NpzbmcwUWczExWoHt3mODd3s0aTMGEERGHA0i6D+geER+l\nz9cA/tIw4VlzUmxBwHxLMZujuZahnM3KfM4/OIQzOI8PWYtOneDrD0sbq5ktKssoqa65ZJH6GNig\nRPFYM1V4ifHqgr25h4s4mQ14m0f5BSdyCePYLJlo57kTZmWTZZTUE5IeknSYpEOB+4HHSxyXNSNt\n2mQ7r4KXeZIduJt9mMNS7MF99OYRxrEZAwZ4op1ZuWUZJXWspH1YsI/3iIi4q7RhWXOQtajdmSkM\n53T6chOf0JGjuYbrOYq5LEWnTvChu5/MGoVaWxiSWkt6JCLuiojj069MyULSSEmfSBqfd2x/SRMk\nzZNUUcu170t6XdI4SVXZfxxrLNq0KZ4sVuRLLuAU3mRD9uVOhnE66zOZVgOOZk4sRYSThVljkmUD\npVmSVlqMe48C+lQ7Nh7YF3gqw/U7RkSviCiYWKzxyTLxbilmcwxXMZn1OYWLuJUD2HLFtxgcw/gq\nVvRkO7NGKkvR+zvgdUkPs/AGSsfVdlFEPCWpa7VjkwCUvQJqTUiWJcf34l4u4mQ25C0e5+f8ZZ1L\n+ee7m3NoQwVpZostS9H7fuBMklbB2LyvUgpgjKSxkvrXdqKk/pKqJFVNnz69xGFZbQ4+uPBrW1DF\n4+zIPfyKQOzJvUy78TH++e7mDRegmS2RLAnjVpIEUQXcGhE3RMQNpQ2L7SJic2A34BhJPyt0YkSM\niIiKiKjo2LFjicOymuSWH69pJdi1+S830o8qtqQ7ExnA1WzC66w9YE/69nNL06wpqW1pkKWA4cAR\nwBSS5LKWpL8Dg0u5nlRETEu/fyLpLmArstU9rAHV1gW1Il9yKhdwPJcTiOGcxoWcwjetV+KGG7yE\nh1lTVFsL42JgZWCdiNgiIjYXpyakAAAStElEQVQD1gPaA5eUKiBJy0taIfcY2IWkWG6NRGUltGpV\nc7JYitkM4Gomsz6ncQG3sz8b8iaDGU7fASsxZ46ThVlTVVvC+CXw24j4OncgIr4CBgC7F7uxpJuB\n54ENJU2VdKSkfSRNBX4C3C/pofTcTpIeSC9dHXhG0qvAS8D9EfHvxfnhrP716JEkikW7n4I9uZfX\n2YSrOYYJ9GALqjiEG/mAzt6XwqwZqG2UVETU8LEQMVdS0X3LIuKgAi8tMo8j7YLaPX38LlTbfNnK\nrrbup80ZyyWcyI48wRtsyF7cw7/YEy85bta81NbCmCjpkOoHJfUD3ihdSNbY5FoV1a3FB9zAIYyl\ngo0ZzzFcxSa8zr/YCycLs+anthbGMcCdko4gGSUVwJbAcsA+DRCblUllJRxxBPzwQ82vr8BX8wva\nIriAUzif0/iKBfM727f32k9mzU1ty5t/CGwt6RdAD5I/GR+MiEcbKjhreL17w6MF/gu3Zg6/5a+c\nzVBWYzqj6ctghvFfuix0nusVZs1TlsUHH2PB5knWDFVWwu9+B998U+iMYA/u52JOYiPe4El+xh7c\nTxVbLnSWE4VZ85ZlaRBrpop1PQH04j9cygn8gsd5kw3Ym7u5N69GAbDTTvDII6WP18zKK8tMb2sG\nBg5M5k5IC7769SucLNbiA0ZxKGPZgp68xrH8mY0Zz73sTS5ZtG6dFLWdLMxaBrcwWoDa6hLVteNr\nTuFCTuBSRHAxJ3E+p/El7Rc6z91PZi2PE0YzN3BgtmTRmjkcxfWczVBW5xNu4iBOZzhT6LrQeU4U\nZi2XE0YzNnAgXHNNsbOC3XmAizmJ7kziabZnT/7Fy2w1/wwnCTMD1zCarSzJYlPG8TA7cz+/ZGlm\nsw938jOemp8s2rVLahROFmYGThjNUrFksSZTGcnhvMLmbMZ/OI4/0YMJ3M0+LLusGD06WSvq6689\nS9vMFnCXVDNTWVk4WbTja07mIk7gUlozl0s4keGczpe0p107+Pu1ThBmVpgTRjMzaNCix1ozhyMY\nyTkM4Ud8zO2tD2T/t4dz8jrrcHLDh2hmTZS7pJqZzz7Lfxb04UHG0YsR/I7JrM+2rV7ghxtuhnXW\nKVeIZtZEOWE0Uz15lTHswoPszjJ8z77cwW7LP80x/9ja3U5mtljcJdWMVFZCJz7kXM7kMEbxBR0Y\nxBVcwwDaLN+GmTPLHaGZNWVuYTRxlZWw6qrQTjOZ3G8ob7EBfankMv7I+kzmSgYxr3Ubrruu3JGa\nWVPnFkYTVlkJRx42l4PnJAXtNfgft3AApzOc91h3/nnt23v0k5ktOSeMJuyeAf/m5TknsQnjeZZt\n2Ye7eJFtFjnv88/LEJyZNTvukmqKXnuNaT135bavd6Mts9iP29meZ2pMFgCdOzdwfGbWLDlhNCXT\npsGRR0KvXrSd8DJ/4HK6M5E72I/8/SnytWkDw4Y1bJhm1jy5S6opmDkTLrkELr4YZs+G449n3csG\n8wUr13pZu3ZwrWdvm1k9cQujMZs7F/72N9hgAzj7bNhjD5g0iYHfXlprslhllWTRQK8FZWb1qWQJ\nQ9JISZ9IGp93bH9JEyTNk1RRy7V9JL0pabKkU0sVY6M2ZgxsthkcdRTTl+/Cbis+i26/Da2/XsG1\noqQkUXz6qROFmdW/UrYwRgF9qh0bD+wLPFXoIkmtgb8AuwHdgYMkdS9RjI3P669Dnz6w664wcyZP\n//421nz/Of791bZFL41wojCz0ilZwoiIp4DPqx2bFBFvFrl0K2ByRLwbET8AtwB7lyjMxuOjj+C3\nv4VeveDFF+HSS2HSJPa5aX9mz6m5oF1dly4ljtHMWrTGWPReE/gg7/lUYOtCJ0vqD/QH6NwUx49+\n882CgvYPP8Bxx8GZZ8LKK1NZWX0xwcIkj4Yys9JqjEXvmv6cjkInR8SIiKiIiIqOHTuWMKx6Nncu\njBwJ3brBWWfBbrvBxIlw+eWwclLQHjw4++2OPtrdUWZWWo0xYUwF1s57vhYwrUyxlMbDD8Pmmydz\nKjp3hmeegdtvh/XXn39KZSVMmVL8VpL33DazhtEYE8bLQDdJ60hqAxwI3FvmmOrH+PFJS2KXXeCr\nr+CWW+D552G77aishK5dkwTQqhX061f8dl26wI03OlmYWcMo5bDam4HngQ0lTZV0pKR9JE0FfgLc\nL+mh9NxOkh4AiIg5wLHAQ8Ak4LaImFCqOBvE//4H/fvDppsmCeKSS+CNN+CAA0CisjJ5OdeiiIId\ncNC2LfP33H7/fXdDmVnDUdT26dTEVFRURFVVVbnDWOCbb+Cyy+DCC+H77+GYY5KC9iqrLHRa167Z\nup8gSRZOEmZWXySNjYiC8+LyNcYuqaZv7lwYNSqZoT1kSDKnYuJEuOIKWGWV+d1PrVole1lkTRZd\nujhZmFn5NMZhtU3bI4/AiSfCq6/CVlvBrbfC9tvPfznX/TRrVvI867DZtm09bNbMysstjPoyYUKy\n1tPOO8OMGXDzzUm9Ii9ZQDJUNpcsslplFRgxwq0LMysvJ4wl9fHH8LvfQc+e8OyzcNFFSUH7wAOT\nPqdq/vvf7Lfu0sVrQ5lZ4+EuqcU1a9aCgvZ33yUF7SFDkqJELTp3Ll6z6NIlGQFlZtaYuIVRV/Pm\nwQ03JAXtM89MuqAmTIArryyaLCCpQ7RtW/h11yrMrLFywqiLxx6DLbaAww6DTp3gySfhzjuT5JFR\n375JPaJLl2SS3iqrJF9Scsy1CjNrrNwllcWkSXDSSXD//cmn+k03JZPuaqhRZNG3r5OCmTU9bmHU\n5uOPk4WaNtkEnn46qVe88QYcdNBiJwszs6bKLYyazJqVTLK74AL49tskaQwZAk1pNVwzs3rmhJFv\n3rxkHOvgwTB1Kuy9d9Kq2HDDckdmZlZ27lfJefxxqKiAQw+F1VeHJ56Au+92sjAzSzlhfPkl7LUX\n/OIXyQy50aPhpZdghx3KHZmZWaPiLqkVV0wm3p1/PgwaBMstV+6IzMwaJScMCR56KPluZmYFuUsK\nnCzMzDJwwjAzs0ycMBpI/qZJXbsmz83MmhLXMEqosjKZ0jFlStLrldsNd8qUZBMl8BIhZtZ0uIVR\nIrmd9XJLmVffOn3WrCSZmJk1FU4YJZJlZ726bKZkZlZuThj1pHqNotgmSZBspmRm1lS4hlEPct1P\nuRZF9ZpFTbxRkpk1NSVrYUgaKekTSePzjq0s6WFJb6ffOxS4dq6kcenXvaWKsb7U1P0Usej0jtxz\nb5RkZk1RKbukRgF9qh07FXg0IroBj6bPa/JtRPRKv/YqYYz1olAtImLBznpdusCNNybH3n/fycLM\nmp6SJYyIeAr4vNrhvYEb0sc3AL8q1fuXSk3zKQrVIrp0SZLDvHlOEmbW9DV00Xv1iPgIIP2+WoHz\nlpVUJekFSbUmFUn903Orpk+fXueA6jKhLn+obMSC+RS7757UJPK5RmFmzU1jHSXVOSIqgN8AV0ha\nr9CJETEiIioioqJjHXfEK5QACiWNmmoVs2bBAw8kNYn87ifXKMysuVHUNpRnSW8udQXui4iN0+dv\nAj+PiI8krQE8ERG17lAkaVR6j38We7+KioqoqqrKHF+h4a+5rqTqWrWqeeSTlHQ7mZk1NZLGpn+g\nF9XQLYx7gUPTx4cC91Q/QVIHScukj1cFtgMmliKYQsXqQscL1So8n8LMWoJSDqu9GXge2FDSVElH\nAhcAO0t6G9g5fY6kCknXp5duBFRJehV4HLggIkqSMOqaAIYNc63CzFqukk3ci4iDCry0Uw3nVgFH\npY+fAzYpVVz5hg1beMId1J4AcjWJwYOTVkjnzsm5rlWYWUvQomd6L04C6NvXCcLMWqYWnTDACcDM\nLKvGOqzWzMwaGScMMzPLxAnDzMwyccIwM7NMnDDMzCyTki4N0tAkTQcy7HVXr1YFPm3g96wPjrth\nOe6G5biz6xIRmRbia1YJoxwkVWVdh6UxcdwNy3E3LMddGu6SMjOzTJwwzMwsEyeMJTei3AEsJsfd\nsBx3w3LcJeAahpmZZeIWhpmZZeKEYWZmmThh1ANJ50p6TdI4SWMkdSp3TFlIuljSG2nsd0lqX+6Y\nspC0v6QJkuZJarRDEAEk9ZH0pqTJkk4tdzxZSRop6RNJ48sdS11IWlvS45Impf+PDCp3TFlIWlbS\nS5JeTeM+u9wx1cQ1jHogacWI+Cp9fBzQPSKOLnNYRUnaBXgsIuZIuhAgIk4pc1hFSdoImAdcB5yY\nbsDV6EhqDbxFsrvkVOBl4KBS7SBZnyT9DJgJ/CMiNi53PFlJWgNYIyJekbQCMBb4VWP/nUsSsHxE\nzJS0NPAMMCgiXihzaAtxC6Me5JJFanmgSWThiBgTEXPSpy8Aa5UznqwiYlJEvFnuODLYCpgcEe9G\nxA/ALcDeZY4pk4h4Cvi83HHUVUR8FBGvpI+/BiYBa5Y3quIiMTN9unT61eg+R5ww6omkYZI+APoC\nQ8odz2I4Aniw3EE0M2sCH+Q9n0oT+PBqLiR1BTYDXixvJNlIai1pHPAJ8HBENLq4nTAykvSIpPE1\nfO0NEBGDI2JtoBI4trzRLlAs7vScwcAcktgbhSxxNwGq4Vij+6uxOZLUDrgD+EO1HoBGKyLmRkQv\nkpb+VpIaXVdgi9+iNauI6J3x1JuA+4GhJQwns2JxSzoU+CWwUzSiglYdft+N2VRg7bznawHTyhRL\ni5HWAO4AKiPiznLHU1cRMUPSE0AfoFENOnALox5I6pb3dC/gjXLFUheS+gCnAHtFxKxyx9MMvQx0\nk7SOpDbAgcC9ZY6pWUuLx38DJkXEZeWOJytJHXOjFCUtB/SmEX6OeJRUPZB0B7AhycidKcDREfFh\neaMqTtJkYBngs/TQC01kdNc+wJ+BjsAMYFxE7FreqGomaXfgCqA1MDIihpU5pEwk3Qz8nGS57Y+B\noRHxt7IGlYGk7YGngddJ/j0CnB4RD5QvquIk9QRuIPn/pBVwW0ScU96oFuWEYWZmmbhLyszMMnHC\nMDOzTJwwzMwsEycMMzPLxAnDzMwyccKwRkfSKunKv+Mk/U/Sh+njGZIadBE5Sb3SobG553st7qqz\nkt6XtGr9RVen9z4sfxVlSddL6l7uuKxpccKwRiciPouIXukyCdcCl6ePe7FgbH29kVTbige9gPkJ\nIyLujYgL6juGBnAYMD9hRMRRjX0FV2t8nDCsqWkt6a/pngFj0lmxSFpP0r8ljZX0tKQfp8e7SHo0\n3fPjUUmd0+OjJF0m6XHgQknLp3tAvCzpP5L2TmdnnwMckLZwDkj/Ur8qvcfqSvYReTX92jY9fnca\nxwRJ/Yv9QJIOl/SWpCfTny13/1GS9ss7b2b6vV36s7wi6fXc+lqSuirZB2Kh3096jwqgMv05lpP0\nhGrYS0RSPyX7MoyTdJ2SBfFap7GMT9/v+CX472dNmBOGNTXdgL9ERA+SWd7/lx4fAfw+IrYATgSu\nTo9fRbKnQ0+SxRWvzLvXBkDviDgBGEyyN8iWwI7AxSRLTA8Bbk1bPLdWi+VK4MmI2BTYHJiQHj8i\njaMCOE7SKoV+GCX7N5wNbEeyb0b3DL+D74B9ImLzNNZL0yUxavz9RMQ/gSqgb/pzfFsglo2AA4Dt\n0hbdXJLVl3sBa0bExhGxCfD3DDFaM+TFB62peS8ixqWPxwJd05VJtwVuX/C5yTLp958A+6aPbwQu\nyrvX7RExN328C7CXpBPT58sCnYvE8gvgEEhWGgW+TI8fly5fAsnig91YsPxKdVsDT0TEdABJt5Ik\nstoIGK5kk6N5JEumr56+tsjvp8i98u0EbAG8nP4elyNZavtfwLqS/kyysOaYOtzTmhEnDGtqvs97\nPJfkQ60VMCP9q7iY/LVwvsl7LJK/xhfamEnS1nUJTtLPSRaO+0lEzEpXHV22DjHlm0PaC5C2INqk\nx/uSrKO1RUTMlvR+3nvU9PvJHD5wQ0SctsgL0qbArsAxwK9J9k+xFsZdUtbkpfsdvCdpf0g+XNMP\nOIDnSFaJheSD9pkCt3kI+H2ua0fSZunxr4EVClzzKDAgPb+1pBWBlYAv0mTxY2CbIuG/CPw8HRm2\nNLB/3mvvk/zFD8lOfUunj1cCPkmTxY5AlyLvUeznyP959pO0WvozrZzWgFYFWkXEHcCZJN1v1gI5\nYVhz0Rc4UtKrJLWE3EZLxwGHS3oNOBgYVOD6c0k+kF+TND59DvA40D1X9K52zSBgR0mvk3T/9AD+\nDSyVvt+5JFvfFhQRHwFnAc8DjwCv5L38V2AHSS+RdF3lWkSVQIWkqvTnzrIM9ijg2lzRu0AsE4Ez\ngDFp/A8Da5B0eT2hZDe4UcAiLRBrGbxarVkjIukwoCIiGs2ujWY5bmGYmVkmbmGYmVkmbmGYmVkm\nThhmZpaJE4aZmWXihGFmZpk4YZiZWSb/D9J2wdrQdeqfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde4e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(np.log(traindata['SalePrice']),plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2U42kUZwV9RBjBzQrauutN50FFeGkYVYrNetxSLqKrLewo6SVZLOjvghcK+k0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\nzHL5/1diT2vhaD21AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde4e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(traindata['GrLivArea'], plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed2ba58>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h6FYpxatPH5g7F/74xzBd+fe+F6b8OPNMGD0atm2Drl3D843U69pr4Ygjwuvg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef1aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(traindata['GrLivArea'], color='b', bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf31bc88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9dzM7DbgJaArc5u7Xph1vCdwBHAm8D3zd3d+KNlTJ5sMPw5DLzJmh5/Tpp2Ho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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf2dfb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(traindata['GrLivArea']), color = 'b', bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tsk6RZiOLlFwyo4/6Enyj38vMpgANgU7J3Nzd1wENYo6NiHruaHZ0pVW/Pvy2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3e0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(np.log(traindata['GrLivArea']), plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 异常值的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xef37a58>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rDeB3ZqVq07N0SkaZqn4oIk8BU4BBSRluVcA2t1kdMBKoE5EIMBCvLE6iPSF5\nH7/2HTmOkX5ed+PmC1VXVxfeUW8KUt/QyGPrtmYJ9al6RcL8+Y36gtKDxw8f2HqXlOz6GeP9V/ds\niZMoXzbvkfU8N38aF1ZXsej5d1u3Ta/knBj7SZ8o+mztDj7346dbj37rH19n9kmjfKsXjDt8gO+1\npp95oivu6dc/AITxwweUJPBY2rQJUmDBRkSGAs0u0PQGPos3cL8Kr2r0g8AcYInbZal7/rx7faWq\nqogsBR4QkduA4cBYvIrTAowVkSOBrXhJBBe7fbIdwwTAb7b6krVb+c7D6wou/dIUi3Hvs29ltKcv\nRgYHU5RveGwjkbDQEofvfO5oJowY2DruAtnTgDdu280Da95Nec8H1rzLlacfnbK2TfI3+yH9Kql9\nfw/ffmhtRrBY9Py7zJ4yOmPFUIDZJ41KCWqzTxpF9RGHpASmC6urOOuOZ1r/rSIhuO3CSZ1+R2Fp\n0yZIQd7ZHA7c57LGQsBDqrpMRDYBD4rIvwN/AX7utv858CsRqcW7o7kIQFU3ishDwCagBbjMdc8h\nIpcDTwBh4F5V3ejea36WY5gOltzt0hSLc/lpYzjpqEPaFGgqI8Lpxw5j+Ya/pbT3ioQYObhP6/NE\nUNuwdTcLHve6xppiynmThnPbn14nEhKaYsr1M8ZxyeQjqBrcm6ZYZnfZR/tbSO/Va4nD82/UZ/1m\n/2ztDr67eH3Gfglrt3zI+dUjMz6Ub5z1d8yeMpq1Wz5k0shBjBnWH6D1bqlvRZhz/uvZlH+rlri3\nJk5n31FY2rQJUmDBRlXXA5/0aX8TL5Msvf0AcEGW97oJuMmnfTmwvNBjmI7l1+3yoxWvt+k9KsLC\nd844hlufeC3jtQMtcb6+qIZbzz+udTXMSEgyUowfqqlLeX7Noxt4+e2dHHPYAJrSosO0Y4eSrV9v\nR8OBLHdCHzH/kfUZ75Vs9JA+WV8bM6x/a5BJSNRMW7flQ8I+2Xlh6fw7Clvt0wTJqgCYovl1u7RV\nU0y5aflfs77e2BLn20l3SU0F3i098pdt+A3VLX/lff60aTshgeTpNNGwcPKYodz8h9Sg533T17zX\necnP17RWJ2iLqsG9ifnM64lpae4obLVPE5Tgp3abstWREzZzaUvJ/0I0xeIIQkVY6B0NUxEWvj9z\nfOtqnpURSUltHj6wN40+k0STNbZ41QRq39/TprThIf0q+eKnqlLaQq6sTqk+6NuTNm1MNuK3iFVP\nVF1drTU1NaU+jW5n6dqtfNen4GV38OmxQ1jz5k6i4RAHmmMgQkU4RFyVy08bw8WTR7UmDGhcaYwp\nlZFQ1mutDAsqQmW48LTh+oYE4ccxAAAdoUlEQVRGpt6yMmXphcqI8OerTrcPe9MtiMhLqlqdbzu7\nszHtMnPSCL48dXQg7923IkxlJES4bQUHCvbM5nqaYsrepphX3DOu7G+O0dgS546Vm3lrewPfdRMw\nG93dlaryvbOPpTKS+b9OY0xpajk4WfO7i/NP1vSbYFoRDgey9IAxpWTBxrRLfUMjv3ju7Q57v17R\nEL2iIb531rGcOf4wYvE4kaCiTQ7NMeWCn63OSAqojISZfOQQ/nzVNL79uaNbu9wqwkKvtEKcjS3x\njBRrSJ2hbxlgpqewBAGTV65VH+t27ffNpirWD8+fyO79zVy3ZENraf9CCmOGgPM+OZwnNr6fMTG0\nWH5HTQSCIf0queL0sa3LPidSmNPduWpzylo7fjP0LQPM9AQWbExO6R+O154zjgkjBrZ+4GbLpirW\njoZG/t/vX826hkw2ceAPG95jX3P+HXtFhAMFrq+TrCLiJQyAV7E58W+QCAyXnzYmI/U70SWWa2no\n5+ZP850Qakw5sW40k1Xt+3taxywS4xDX/G4Dl9yzurVa8ZB+lVw3Y1yHjasc2q8i6/LR+f5YkwNN\noltr9kmjWrvmEu0tcSVa2JI1Ke+3/IqTUchasfniyaMy/h32N7e0donV7dqPpgVmjWtrMLIMMFPO\nLNgYX0vWbuXsO57xncjY0BhrrVZ8/+p3WLBsU4d0pV1YXcVJHz/Udy0agBOOGFTwe8Xiyg/Pn8iV\npx/Nr79yIi3uPZtiSkscfGp4tqqMCOmXc9GJIxnctyLr0gQJobQdJSlw9q0ItyYaJDTGlL45ljKw\nCsymXFg3msmQ6O7JN4EyHBJueGxjwRMtczlv4nAumXwEb21v4NNjD2XVazsythnUp/AFV2MKVz74\nFxRQLagWKJEQzD3lKPpWRLhjZW1KivNDNXV89hPDctYOe/6NesIiNCcdrVfkYDfa3qYYvaKhlDTn\nXtFQ1jEmq8BsyokFG5Nh47bdWbuykjW3xImGQzTF2j8gv3TdNh5bt41c7/TUax+06T3bGgNb4vCT\np970fc1LT5asmWPX/e4VFq3OzDxLzizLlmHm115IBeZciRvGdDXWjWZSLFm7la8vqmFfARldXz35\nSGIdNCk4DjkDDUBzCeeNNsfjjB8+wHfJ5V17m3wDTWUkdUnmtizZnG+Bt2zLWhvTVdmdjWl1cKGz\n/AGkMiJ84fgq+lRE+PGK19tRHa1r61MRJq7aGhT8aoctrtniu+/VZx2b0e1VaO2xXPNvbN0Z0x1Z\nsDGtCims2TsSIo7yxeqRnHvns4RFyjbQVIThp5cez/jhA1M+xJPTnQEmjfRPXDh5zKG+7en7Z9sm\n2/ybdVs+tHVnTLdjwca0KqSwZkwVEeGBF97NurZLOYiE4IcXTOKUoz+Wd9sxw/r7LpKWvqxAW2W7\nC7KqA6Y7skKcjhXi9Cxdu5V5j6wnFo/nTA8uVzd/fgLDB/cpamnm2vf3ZCySFpTEfyfLVDOlVmgh\nTruzMSlmThrBuMMHcNbtT5f6VDpdZVj4xPCBTMzSLZaP3yJpQbF1Z0x3Y8HGtEqk0u7e30yvaITm\nxpZSn1KnkpB0q66oQsZ+jOkqLNgYAO5f/Q43PLaRaDhES1yJ5Rm7CYekQ2uiBS0cgqumH8utT7xO\nk0+FgoqwWAFMYwIU2DwbERkpIqtE5FUR2SgiV7r2Q0RkhYhsdr8Hu3YRkTtEpFZE1ovI8UnvNcdt\nv1lE5iS1nyAir7h97hBXGyTbMYy/+1e/wzW/29C6tkti5nyuP47uFGgA+kQjnHjkEK6fOS7jtYpI\niOX/8umCxzy6QgmZrnAOxrRFkHc2LcC3VfVlEekPvCQiK4B/BJ5U1ZtF5CrgKmA+cBYw1v1MBu4C\nJovIIcD1QDVe1ZGXRGSpqu5y28wFVgPLgenA7917+h3DpKlvaOSGZZsy2sst06yxJUbV4N7eeIzC\nDY9tJBLyinJef+64gsda8pWQ6YxZ/UvWbmXe4vWtd5e3nl98coBVITCdJbBgo6rvAe+5x3tE5FVg\nBDALONVtdh/wFF4gmAUsUi89brWIDBKRw922K1R1J4ALWNNF5ClggKo+79oXAefhBZtsxzBp6nbt\npyIsNJX58MyM4w5v/TC9ZMoRANywbBMVkRALHt9E/16RgpZwzjWZsjNqmdU3NPKdh9fRnFSL59sP\nrytqQqfVXjOdqVPK1YjIaOCTwBpgmAtEiYCUmMgwAkieil3n2nK11/m0k+MYJk3fijD7e0CO82Pr\nt7WWdKlvaGTB45toaomnVLAuZgnnEMLGbR9R39DIvMW5K0J3hI3bdqcEGvBWFd24bXeb3ic5cAZ5\nvsYkBB5sRKQf8Ajwr6r6Ua5Nfdq0iPa2nNtcEakRkZrt27e3ZdeycP/qdzj7jmfIUtG/rDTFaP0w\nzVd3LBu/yZT7mmN8fVEN8xavS6kSXeh7tl22AqltW+Kh2H8DY4oVaLARkSheoLlfVf/HNb/vusdw\nvxOlfOuAkUm7VwHb8rRX+bTnOkYKVb1bVatVtXro0KHFXWQ3lZwU0FMkPkyLnYGfKCFTGUn9YG9s\nifPkXzO/rDTFOn5W//jhA4ik/V8bCXntbWFVCApnyRgdI8hsNAF+DryqqrclvbQUSGSUzQGWJLXP\ndllpU4DdrgvsCeAMERnsssrOAJ5wr+0RkSnuWLPT3svvGD1Grv9B6hsaueGxjSU4q9JqbInxl3d3\nsWtvU8HVl9PNnDSC/55dTZ8cC54lXH7amA4fdB/Sr5LbLpxEZUToEw1TGRFuu3BSm4/TlgrUPZlV\n1+44gZWrEZGTgWeAV6C1YuD38MZtHgJGAe8CF6jqThcw7sTLKNsHfFlVa9x7fcXtC3CTqv7CtVcD\nvwR64yUGXKGqKiJD/I6R63zLqVyNl620jrCEiGmcW8+fmDLw+/Tr2/nyL15o83ov5WT2SaO48vSj\ni8rEqm9oZOotK1MWQUsXDcPqqz8b2Id3R2WRWTZadn7/nXtFQzw3f5r9WyUptFyN1UZzyiXY1Dc0\nMvk//pSSuhwJwZrvfbY1Y+pfH1zbtsGtbqAiLICiSMYAejZ/+rdTii4vk6hNFg4JexszEyxuOm9C\na9ab6Z7WbfmQS+9Zw56kShr9KyP8+muTiy5pVI6sNloPtXHbRxlzZFri8Pwb9Rx7WH++/VD5BZqw\nwD1zPsX44QN4rnYH3128rqA1edZu+bA12OT6hu/3WnJtsg1bd7Pg8U2EQ16gu37GOC6ZbIGmu7Nx\nrY5lwabs+H/IXvnbtaBall1nMYU3tzcwfviA1iDwwJp3uXNVLRXhEE2xeEamGBxchybXfJNcryVq\nk00cOYjpEw6z7qgyk2tNIdN21o3mlEs3Wu37e5h++zO0dLNyMu3VOxpCISUYJN+R3P7k6xnrzdw4\n6+9y9ssD1mdvbFwrD+tG64ES38LD4tUKKhfRcP5xmP3NqTP6gZQPiBtn/R2zp4zOWG/Gb3XSRIr0\n7v1NhCQ1zTl5RUz7EOoZyrm6dmf+DVuw6aaS/0jAm1k+r8Cxiu5m3pnHcNPyvxa0bTQU4v417/KT\np2ozur7S15upb2hk9/4mmmKpA/zN8Tgbtu7mxmUbM/49E332VurFdHed/TdswaYbSv4jOdASIx5X\nKiLhsgw0AGveqi9426ZYnIWrNtPYor71yxKS/w3j6mXs9Y5GaI7HufbccSxYtinj37My4s1F2bW3\nie8+vI6mWO5jGNNV5avzFwQLNt2M3x8JUNb1zf70qn8poV4RIaagqq2B4rJTx3D302/S2HKwIzG5\nDEvdrv30rQhn/BtWRkIsvOR4xg8f4Nu11ica5qf/cAK79jVx9n89m1F5Ibl7zZiuLlf3sQUbA3h/\nJC09oZhZmpBAcs5DSOCHF0zi2MP689e/fcSOhiZOHnMog/tWsPCp2pR9E91iX7z7eaKhEI0tsYyc\nvYpwiIG9o63/o6WnvMZRhg/sxdxf1dDkk9lmKbGmOylFWrcFm26muSVWdmvNFGLGcYfzh41/QxWa\nYko0JPzbQ2uJxbU1CEVCcNuFkzLSVa89ZxwLHt+UcTeYLPl/tGwpr3ubYhnfBsFW+TTdTynSui3Y\ndDOPv/JeqU+hJP6w8W9cdurH+c8nvbuWRp/stJY4fOfhddwz51Msu/xktu0+QKJAuF+QSEiMxST/\nj5Y8aTORqVPf0JjxbTAaFpb/y6eLrkRgTKn4/Y0HyYJNN1DzVj1Pb96BAL/48zulPp2SaGxRbvtT\nbd7tmmLKN371Ek2xGCJCr0iYplicWNw/0PSKhrhq+rGt6dLJ0lNeE98Gv/XQ2pS7y03vfWTBxnRL\nnZnWbZM6na46qfPSe1bzbG3h2VjGXzQsCPguqdC3IkxMtTX1M1/pmr+/eWVKRQKb6Gl6MpvUWQZq\n3qq3QNNBekXCLLzkk6zbsps7V20mEg61FtDc2+T9nvfIevYcaGHB45uyzj3wltEOpQQby0QzJr9O\nWRbaFOfOVfm7jYwnEsq9UmVzPM744QO54vSx/Pmq07lhxnj6pq1JExbh+49tzLlUshVnNKY4Fmy6\noPqGRp5+/QOefn1HqU+lW4iGhYr05SuBcIiMhcHqGxrZuG03vaIhDqTNTdrXFMsoi5O+VLItOmZM\ncawbrQupb2jk/jXvsnDVZsKhUJbcqZ6jIiwFL1sdi2fO9n/8ipPZ2xRrHXtZsnYr33l4XdY6a36t\nTbFYxl1LZ2fxGFMOLNh0Ed7qmuuTxgLKtyJAPgJcfdax/PCPrxW0/fdnjqd/ZSRjzkB6HbR5i9cX\nvLBawuWnjfUNJuVcnNGYIFiw6QISJWj81lzpiRToWxnh+hnjueZ3GzJeDwv0ioZpjsW5fsb41oXK\n/ObFJJ7X7dpPOM+4TrrKSIiLJ4/qiEsypscLbMxGRO4VkQ9EZENS2yEiskJENrvfg127iMgdIlIr\nIutF5Pikfea47TeLyJyk9hNE5BW3zx0iXi34bMfoypLHBHqScI7P/hse28j0CYfxvbOOzXgtEg5x\n16Un8PzVp6csvZxYyCzRZTb1lpVces8apt6ykg1bd2d0tSX0rQzTKxpi9kmjiCadVCwe57laGzcz\npiMEmSDwS2B6WttVwJOqOhZ40j0HOAsY637mAneBFziA64HJwInA9UnB4y63bWK/6XmO0WX1rQin\nLNDVE4QFbpw1IWvAiYa9gfnJRw3JyBpLr2OWLrlYaSKrbMHjm7huxriMu5sLq0fwwNem8Nz8aVx5\n+tEkv9wSJyMbzRhTnMCCjao+DexMa54F3Oce3wecl9S+SD2rgUEicjhwJrBCVXeq6i5gBTDdvTZA\nVZ9Xb1bqorT38jtGl7W3KUZlrq/53VRIyAgUCSJQt2sfkbD/n2BMlarBvaka3JuY+q8pk02iom2y\naCjEyMG9SU9aW7ruvdZuN28OTThjv55652lMR+rs1OdhqvoegPv9Mdc+AtiStF2da8vVXufTnusY\nXVbfirBvJlR3kS1OxpWM9OKEljjc9b9v+o5TJRe2LCbVONtcGJCcwcTm0BgTnK6SIOD3caVFtLft\noCJz8briGDWqNAPB969+hxuWbcIbclIqw0JMFVVvELypJUZX72HLleAlApVhISwh9hWw5k5FJMTy\nK05OySRra6pxtoq244cPyBtMLjt1DHeuqqUi3DmVcI3pKTo72LwvIoer6nuuK+wD114HjEzargrY\n5tpPTWt/yrVX+Wyf6xgZVPVu4G7waqMVe1HFun/1OxnZVjH1Imk0EqIpFucLJ1Tx4It1/m/QDfSO\nRlh4yScB4euLarJm3PWpCBOLK5efNobBfSsyXm9rqnG2AJWtrHryyp2gzD3lKC6ePMoCjTEdpLO7\n0ZYCiYyyOcCSpPbZLittCrDbdYE9AZwhIoNdYsAZwBPutT0iMsVloc1Oey+/Y3Qp9Q2NXLc0M623\nJa40x2F/c5zmmHZaoAlqyChRJuaUo4dy6/nHURnJPFBlRJhz0hGAcvfTbzL1lpUsXbu13cdOzk5L\nmDlpBM/Nn8avvzaZ5+ZPay28mZxQ0NiiGQuwGWPaJ8jU598AzwPHiEidiHwVuBn4nIhsBj7nngMs\nB94EaoH/Bv4ZQFV3AguAF93Pja4N4JvAPW6fN4Dfu/Zsx+hSNm7bTVdacLONcx0Ldu2541o/7GdO\nGsGfrzqdb3/uaCojB8dgrpsxnl/8+W0aWzRrTbKOlB6EsiUUWGKAMR0nsG40Vf1SlpdO99lWgcuy\nvM+9wL0+7TXABJ/2er9jdD3dL/vsU6MHsfbd3TT7zFcJixAJpS5q1rcizIThA1O2G9KvkitOH8vF\nk0elTLjs7PXQk1ligDHBs0KcJTJ++IBuF25e2foRP/7ixJSJjwkimhE/E+nLfpLvLkr9YW/FNY0J\nXlfJRusR0hflmvP3R/DLTlp5Myxt6yqLhoUQqXcq0VCIAb0rWjPnklVGwnzjMx9n4VO1bV7TvBTr\noaez4prGBMuCTSdJznZKfJhOO3ZYpwUbRKDAVVmjYeE3X5vMpfe+kBKhvMH+AVw/YxzXPJqeRadc\nPHlUSvdYR2SPdSYrrmlMcKwbLSD1DY2s2/Ih9Q2NvuVT5j2ynuEDe2XMaA8L9K1o+3+W9BqTIfHe\nq39lhMpIqE2B5kcXTOTIof247NQxVEYko2vpkslHcNN5E6gIC30rwimv+WWAFao9+xpjuja7swlA\n+l3MZaeO8R0A39sU47YLJ/HdxesIS4iYxrnu3PEseHxTm48ZEWhKiidx9aoWL7zkeD7a38y8xetz\nTqr8+qeP5NNjD2X88IE8W7uDqbesdBla4jvn5JIpRzB9wmHW7WSMKYgFmw6WfBeTCC7e8s7+9b0m\njhzk2310w2MbiYSFljj882eO4r9W1uYcc2nySaOuCIdYu+VDFq6qzbt8wRerRzJmWH/f81/4VK1v\nqX3rdjLGFMq60TqY35yNinCIy08bmzXbKb00/oLHvfI1+5rioMrCp94oah5MUyzOwlWb8waa2SeN\nai0PY3NOjDFBsDubDpYtjbeQwfPku4qEQpdFTta30iv9ctmpY7j76TdpbGnx3a4yHOJHF07k3InD\n856/zTkxxrSHBZsOli+NN1e3k9/kxlx6R8O0xOOoKr2jEZrjca49ZxwTRgxsDQ65yq5ICE76+JA2\nnb8xxhTDgk07pc+dgeLTeP3uKhIiIQiHQoRDQiyuXHdualBJHCvxGDIDx4GWWEpgyhZEks+/b0WY\nvU0x6hsaLeAYY4pmwaYd/ObOzJzkLauTGDxPpEC3tTS+KjS2xKkIC6GQt75LrgCWXrk4+XyS9wMK\nCoJD+lXybO2OrNdnjDFtIVrg/ItyV11drTU1NQVvX9/QyNRbVqaMr/SKhnhu/rTWD/FcwSiX+1e/\n42WjhYSWuHL9jPFcMuWIdp9PW3T0+xljypOIvKSq1fm2s2y0IuXL2so2kTNfJeP6hkYWPL6Jppiy\nrzlOU0xZ8PimvPt1dBaZZaUZYzqSBZsi5cvaKvbDutj9OjqLzLLSjDEdyYJNkfJVCi72w7rY/Tq6\ncrFVQjbGdCQbs3HaOmaT4JeNlrB07daMFOJCxmyK3S/f+RSjo9/PGFNeCh2zsWDjFBts8in2w9o+\n5I0x3UGhwcZSnwNWbP0wqztmjCknNmZjjDEmcGUbbERkuoi8JiK1InJVqc/HGGN6srIMNiISBhYC\nZwHjgC+JyLjSnpUxxvRcZRlsgBOBWlV9U1WbgAeBWSU+J2OM6bHKNdiMALYkPa9zbSlEZK6I1IhI\nzfbt2zvt5Iwxpqcp12w08WnLyPFW1buBuwFEZLuIvBP0iXWyQ4EdpT6JTtATrtOusXyU23XmLtzo\nlGuwqQNGJj2vArbl2kFVhwZ6RiUgIjWF5L93dz3hOu0ay0dPuc505dqN9iIwVkSOFJEK4CJgaYnP\nyRhjeqyyvLNR1RYRuRx4AggD96rqxhKfljHG9FhlGWwAVHU5sLzU51Fid5f6BDpJT7hOu8by0VOu\nM4XVRjPGGBO4ch2zMcYY04VYsOlmROReEflARDYktR0iIitEZLP7Pdi1i4jc4Ur2rBeR45P2meO2\n3ywic0pxLdmIyEgRWSUir4rIRhG50rWXzXWKSC8ReUFE1rlrvMG1Hykia9z5/tYluCAile55rXt9\ndNJ7Xe3aXxORM0tzRdmJSFhE/iIiy9zzcrzGt0XkFRFZKyI1rq1s/l47hKraTzf6AU4Bjgc2JLX9\nALjKPb4KuMU9Phv4Pd68oynAGtd+CPCm+z3YPR5c6mtLup7DgePd4/7A63hlh8rmOt259nOPo8Aa\nd+4PARe59p8C33SP/xn4qXt8EfBb93gcsA6oBI4E3gDCpb6+tGv9FvAAsMw9L8drfBs4NK2tbP5e\nO+LH7my6GVV9GtiZ1jwLuM89vg84L6l9kXpWA4NE5HDgTGCFqu5U1V3ACmB68GdfGFV9T1Vfdo/3\nAK/iVYAom+t059rgnkbdjwLTgMWuPf0aE9e+GDhdRMS1P6iqjar6FlCLV66pSxCRKuAc4B73XCiz\na8yhbP5eO4IFm/IwTFXfA++DGviYa89Wtqegcj5dgetK+STeN/+yuk7XvbQW+ADvg+UN4ENVbXGb\nJJ9v67W413cDQ+ji1wj8JzAPSKx1PoTyu0bwvij8UUReEpG5rq2s/l7bq2xTnw2QvWxPQeV8Sk1E\n+gGPAP+qqh95X3L9N/Vp6/LXqaoxYJKIDAIeBT7ht5n73e2uUUTOBT5Q1ZdE5NREs8+m3fYak0xV\n1W0i8jFghYj8Nce23fk6i2Z3NuXhfXcbjvv9gWvPVranzeV8OpuIRPECzf2q+j+uueyuE0BVPwSe\nwuu/HyQiiS+Byefbei3u9YF43ald+RqnAjNF5G28yuvT8O50yukaAVDVbe73B3hfHE6kTP9ei2XB\npjwsBRKZK3OAJUnts132yxRgt7udfwI4Q0QGuwyZM1xbl+D66X8OvKqqtyW9VDbXKSJD3R0NItIb\n+Cze2NQq4Hy3Wfo1Jq79fGCleqPKS4GLXCbXkcBY4IXOuYrcVPVqVa1S1dF4A/4rVfUSyugaAUSk\nr4j0TzzG+zvbQBn9vXaIUmco2E/bfoDfAO8BzXjfhL6K16/9JLDZ/T7EbSt4i8i9AbwCVCe9z1fw\nBlprgS+X+rrSrvFkvO6D9cBa93N2OV0ncBzwF3eNG4DrXPtReB+ktcDDQKVr7+We17rXj0p6r2vc\ntb8GnFXqa8tyvadyMButrK7RXc8697MRuMa1l83fa0f8WAUBY4wxgbNuNGOMMYGzYGOMMSZwFmyM\nMcYEzoKNMcaYwFmwMcYYEzgLNsZ0ABEZJiIPiMibrmTJ8yLyeZ/tRktSxe6k9htF5LMFHOeTIqJd\nsfKxMblYsDGmndwk1N8BT6vqUap6At4kxqq07bKWh1LV61T1TwUc7kvAs+6377mIiP1/bboc+6M0\npv2mAU2q+tNEg6q+o6r/JSL/KCIPi8hjwB+zvYGI/FJEzheRs0TkoaT2U92+iaB2PvCPeDPNe7n2\n0eKt/fMT4GVgpIic4e6uXnbH7+e2vU5EXhSRDSJyt+QoOGdMR7JgY0z7jcf7kM/mJGCOqk4r4L1W\nAFNc2ROALwK/dY+nAm+p6ht4tdTOTtrvGLyy9Z8E9gL/F/isqh4P1OCtKQNwp6p+SlUnAL2Bcws4\nJ2PazYKNMR1MRBaKtwLni65phaqmr0HkS73S+n8AZrhut3M4WFPrS3gFLXG/k7vS3lFvbRTwCnqO\nA55zSxjMAY5wr50m3iqYr+DdkY1v+xUa03a2xIAx7bcR+ELiiapeJiKH4t1RgHen0Ra/BS7Dq3j8\noqruEZGwO8ZMEbkGr77WkEQByLRjCF6ASxnXcd1uP8GrxbVFRL6PV4/MmMDZnY0x7bcS6CUi30xq\n69OO93sKb+nvr3OwC+2zwDpVHamqo1X1CLwlGM7z2X81MFVExgCISB8ROZqDgWWHG8M532dfYwJh\nwcaYdlKvmu15wGdE5C0ReQFvGeD5WXY5RkTqkn4uSHu/GLAMOMv9Bq/L7NG093kEuNjnfLbjJRH8\nRkTW4wWfY9VbN+e/8SoN/w54MX1fY4JiVZ+NMcYEzu5sjDHGBM6CjTHGmMBZsDHGGBM4CzbGGGMC\nZ8HGGGNM4CzYGGOMCZwFG2OMMYGzYGOMMSZw/x/w5HKVtEbmkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec4ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8xzW0vLvTN5MNyHhjaBT47YoNWe/j6YhEsvZO4ofx+oeD1Ia8IcPPT6n+wuS5\n3KhrTDko5DDaEcC9LmssAMxX1UdEZC3wgIj8C/AycLfb/m7gNyLSgtejuQxAVdeIyHxgLdAFXOuG\n5xCRrwCLgSBwj6qucZ81K80+jI/YPTZBkbQlY/yEAkJzmgrOrdv3cvnkUfzH0nU53xjqJ6qwvGVb\n1rVuil3dOduaO8VgS16bSlKwYKOqq4CP+LSvx8skS27fh7dOjt9n/RD4oU/7Y8Bjue6j2mW7APq9\nHn+PTU91RZVJIwenXaDs6ZZtRPIs4Nn9WRHlugXeDZpPt2zLeHEtVnXncrjI25LXptJYFYAqke0C\nmO51v3VscvWts47pTl++bsFKghIgotGEBdJ60anp1t4VZdZDq3i6ZVuvL6697ZGUy0Xelrw2lcaC\nTRVIdwEcf8RAdndEutd/8btAehP5Pa8ZUxMUfrTkDfr3CzEgHALEywNUL6GgN0HMz/++uoX+tYlT\njD29uMYH3I5IlK+cPpbLJ4/q0cW5XC7ytuS1qTRFyUYzhZXuDv5zb/8LV9y1gnNuf4rkW2YCIqzZ\nvIP6AWG+cvq4Hu+z0xXd/M7Dq/nm/Gbau6Ls6YjQ3uUlCPSvDfb4Rs9s2pO6ST25uCaX5GnvivLj\nJW/wsVsfT7v0tJ9yucjbktem0liwqQJ+F8B9nVE6IsrO9i46I6SkIe/piHD1vCYWNW/inImH92r/\nyTdlBgNeosFtFx1HTR+WPrv+7GPyvrimC8jtXZpQDSGbcrrIXzBpREKNOksOMOXMhtGqQPwqmzWB\nAO2RKKKa0hNIFrvQzv1CI6FAatDI1+72CCvWtzHzE0fRvzbIVfNezP6mLK48eRQzTz2KacccSvPG\n95k0cjBjDzs45/dnKqnT02GwYme+ZWJLXptKYcGmSsRfAPvXBjnvjqdTVrP0UxMIsHn7npwDTVCE\nSA7FM//1T6/Rv1+IsE99tXxcOWV0j7LAkhMBYgH5ugWrUlYgzWcYzC7yxvSMDaNVkdhd9rEMsXAo\nfW2ymD0dEb79h9U92Ity9cePzGnLmxatYchBNT347PRiac/JN4/6DX/FKhpccdcKps5Z2j0nc8Gk\nETxzwzS++cmjCYek5MNgxhxIrGdTpWI9nbv+sp47n1yfdrtceinJ7n3ubcKhQEoPIVlnRHn8tS09\n/nw/QweEc8oCy5aaXD8gzFfPGMflk0eVxTCYMQcK69lUoHQlSpLb6weEmXXOh/nhpydSGxTqanr/\nzx1R6OiKZg00MQ82tfZ6n5c2NnDyUfU5ZYH5rs0jwrLXtiT8vOoHhGkYUkfr9r1W6sWYIrCeTYVJ\nN2+RaT7j81M+xPSJh7Nm8w5hXvL+AAAaqklEQVSuntdEe1cf3GmZo95WEPjsRxuYc9HxtO1qZ9ox\nh/KYK/4JXhBK7pX4JQLs7ohw0x/X8N2Fq3P6eRlj+p71bCpIuuWb0xXDbNvV3t3bATj16EP56OhD\nSnwWPdPVpdz33Nt87NalCYEGYH5Ta0qvJKEoZ9yS07vaIzn9vIwxhWE9mwqS7u71dMUw/+sv67nn\n6b9SEwwQUeUbZx7N0y1tpTj0vD308iYeetn/pst0Kcux+aplr23hpj+uYVd7JOE9mYqH2vyNMYVh\nPZsKku7udb9imHs6uvjFk+vpiCi7O7y/6ucsfq2Yh1twmVKW6weEOf3YQ1PWuUn387JSL8YUlgWb\nCpLu7vX9qc4BDqr1lk4WSU17zrCWWcWpDZI1ZTnbz6scqgAYc6CwYbQKk+7udY39VwVFCfgEm2pR\nkyaY+kn38yqnKgDGHAgs2FSg5LvXY4kDXpZZbH6ieBlnxeatHrp/nZtsgSLd3f5WBcCY4rFhtCqQ\nrshkpQuHhGs+MYZgmk5Me1eU+1dsKO5BGWPyUn1XqANQpiKTlSocEv7t4uP58iljCGWor3bHsnWW\nsmxMBbBgUwViE+G51EKrBALcd9Xk7pVEazMEm9pgkNbte4t3cMaYvBQs2IjISBFZJiKvisgaEfma\naz9ERJaIyDr3fYhrFxG5XURaRGSViJwQ91kz3PbrRGRGXPuJIvKKe8/t4maN0+2jml0waQT/dWUj\n/fqgJE2pKXD53c+zqHlT1l6bX8pyunI+xpjSKeSVqQv4pqp+GJgCXCsi44EbgMdVdRzwuHsOcA4w\nzn3NBO4EL3AAs4HJwEnA7LjgcafbNva+6a493T76XDEvbMn7Sn4+YfiglBU5K1WHW/ETSEhTDgW8\nbLR0KcvpKj4bY0qrYNloqvoO8I57vFNEXgVGABcCp7nN7gWeAGa59nmqqsBzIjJYRI5w2y5R1fcA\nRGQJMF1EngAGquqzrn0e8GngTxn20aeKWV8reV+XNjYwv6k1Zd+XT27g189Ux6R5Z1eU1u17U9KU\nAd+U5WwVn40xpVOU1GcRGQ18BFgBHOYCEar6jogc6jYbAWyMe1ura8vU3urTToZ99JliXtj89jXv\nWS+gxO97574u7l+xMe3nVJqIQmeXl8odS1NOXhQtXrpyPlaGxpjSK3iwEZEBwEPA11X1gww34/m9\noHm09+TYZuINwzFq1KievLWoF7bW7XsJBTJP/gcEZi9a3WdLO9cEoLMMhuRWb95B45H1QPaeZLpy\nPlaGxpjSK+hssojU4AWa+1T1v13zu254DPc9trpWKzAy7u0NwOYs7Q0+7Zn2kUBV56pqo6o2Dhs2\nrEfnVqgLm98c0OpNOxKKSfrZ0xHts0AD5RFoAP71sVdZ1LwpbcXr5DVqqr0MjSU/mEpVsJ6Nywy7\nG3hVVX8S99IiYAZwq/u+MK79KyLyAF4ywA43BLYY+Ne4pICzgBtV9T0R2SkiU/CG564E/iPLPvpM\n7MJ2fdJf2r25sN333Nvc/MhaaoNCV1S57aLjmDp2KLc8urYPj7yydES8IcK5X2hM25OE/XM41VyG\nxtbgMZWskMNoU4EvAK+ISLNr+zZeAJgvIlcBG4BL3GuPAecCLcAe4IsALqjcArzgtvtBLFkAuAb4\nNVCHlxjwJ9eebh99qi8vbPc99zbf+cNqADq6vLZ0F9lqVxuAjrjT9aojqG9PcvWmHXx27rMpF+Bq\nCjJgyQ+m8hUyG+1p/OdVAM7w2V6Ba9N81j3APT7tTcBEn/Y2v30UQl/U12rb1c7Nf1yT0h4MCKDs\n68o8hFZNzjx2GE+/2UZ8DndnNMqE4YNSepLfO288tzyy9oC4AFvyg6l0VoizlzJlR+WqdfteaoIB\nOiKJQaUzogwfVIcXh6tfKABzLj6e5S3buoNKRyTCtaeNBVJ7kms2f0Ag6e+Zar0AW/KDqXSVf7t5\nCfXVDYQNQ+rY25nae5l9/nh2d0Soq6muvwkCAn6VdW6+YCL1A8JcMGkEy2dN4+pTxwDC3KfWd/98\n6weEOX7kYJ5u2cbV85rYk/Rzq9YL8IGQ/GCqW3VdxYqoL8fQt+/uIOrTeZk8+hAA2qtsGC2qEAwK\nIVXCNUE6I8rs88czfcLhrNz4fnew+M8nWmjvitLelfjzBdySCol/6YdDUtUX4GpOfjDVz4JNnvpy\nDP3plm2+7Xc++SaPvvIOgYB4dzhWkc6IEg4FuPPzJzJh+ECebtnG1DlLu+djrj1tbMbss+TXDqoN\n8osrTuDUo/v8/t2yYmvwmL7UF9MAubJgk6e+HEPvF/IfzVy0crNbKKw6BeNu8E3uJd6xrIXke3Tj\nf77JP/uoKhOGDyrsARtTRYqdSm9zNnnqyzH0Af38Y34wS9WASrenM8LV85q4f8WGlMXfQkHh4hNG\nEg6lFt20+QtjeieXm6T7mvVseqGvxtAH1tX6tldLBedM2rui3LFsHclZ8rvbI/yheRMgXDFlFCcf\nVZ/Qc7H5C2PyV4pUeuvZ9NL23R2se3cn23d35P0ZE4YPJHkkLRSAb511dC+PrjIERTjt6GGEQwH6\nh4Pd7bs7IrR3RbnzyfX8430vpWT8xTLTLNAY0zOlSKW3YNML3//DK5z506f41oJVnPnTp/j+wlfy\n+pz6AWFuvnAiNQEIBwPUBoWfXDqJyWPq6V8bzP4BFW5PZ5TFa9+lvSvKpBGDfVcc3dUeYV9nlOsW\nFLarb8yBoBRD0TaMlqeWd3cy77nEdWPmPbuBK6eMZuxhB/fosxY2b+KmRWtc8csoQXetbRhSR6SK\nb+isCwl7uxLPb/n6tozvae+Kcv+KDXz1jHE92lcxs26MqQTFHoq2nk2e0qUrp2uPF1+5t21XO9cv\nWJWQdRZRuG7BSsBbpbKa+jahAPSrCfDDz0zk6lOPyusz7li2rke9G1u90xh/xRyKtp5Nnoam+cdJ\n1x6TnG547WljfbPOghLoXqVy43u7+bc/r+uT4y6l2iDcNeOjTBg+iPoBYVre3cntS1vy+JxgzhOZ\nVsDSmPJgPZs8HXu4/1BZunbwTze8Y1kLXZHUtLOOSKR7sm6yWzys0l120ihOPfrQ7ov8kP61nPt/\nDuvx5/RkIjOWdRMv/uZQY0xxWLDJ0+6OSPfcSkxQvPZ0/C58tcEAV51yZNr3LGzexBX3PJ+yr3JT\nVxPgoo8MJxwSDqoJEBRSMuzmN7V2D3/Fhrb+8kYbtcHkcprphUM9m8i0ApbGlAcLNnnqXxtMqSAT\nUTJmj6W78J08ZmjK++pqQqzZ/AHX/b6ZfZ3Rsq9Wo8C3PzWe7583gS6F2lAgZeXQWI8iuYfXEYkS\nCkpKcIpXG4RvfvJonrlhWo/ucrYbQI0pDzZnk6fdHRECQkIBzYDr2aTLfIpd+K5bsIpgQIi41Tgn\nDB+YknXWGY1y7zN/JUNHqSzUBgMEAl4iA8Atj66lI8361Pu6vKFBvxvK+oWC/PzzH2H91t38yyNr\niU9SqwkKd81ozLvumd0Aakz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E5GwReUZE3hCRRhE5CvgycJ1778fwqvy2Aqjnlb7/qRiT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\nMQVnwcYYY0zB/X9wHJ4dxrYdzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe20fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bVWWRsrIyHn30UWPNxsxZoaamBqvVanr8HODDg4NYwbCNf+lLX4roc++990YI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef374e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把这些房屋特征和'SalePrice'的相关性可视化\n",
    "traindata.plot.scatter(x='GrLivArea', y='SalePrice')\n",
    "\n",
    "traindata.plot.scatter(x='TotalBsmtSF', y='SalePrice')\n",
    "\n",
    "traindata.plot.scatter(x='1stFlrSF', y='SalePrice')\n",
    "\n",
    "sns.boxplot(x='OverallQual', y='SalePrice', data=traindata)\n",
    "\n",
    "sns.boxplot(x='GarageCars', y='SalePrice', data=traindata)\n",
    "\n",
    "sns.boxplot(x='FullBath', y='SalePrice', data=traindata)\n",
    "\n",
    "sns.boxplot(x='TotRmsAbvGrd', y='SalePrice', data=traindata)\n",
    "\n",
    "sns.boxplot(x='YearBuilt', y='SalePrice', data=traindata)\n",
    "\n",
    "sns.boxplot(x='YearRemodAdd', y='SalePrice', data=traindata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 降序后，删除'GrLivArea'最大且和'SalePrice'不成正比的两个'Id'\n",
    "traindata.sort_values(by='GrLivArea', ascending=False)[:2]\n",
    "traindata = traindata.drop(traindata[traindata['Id'] == 1299].index)\n",
    "traindata = traindata.drop(traindata[traindata['Id'] == 524].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 筛选出所需要的columns\n",
    "finaldata = traindata.filter(['OverallQual','MSSubClass', 'KitchenAbvGr', \n",
    "                               'OverallCond', 'GrLivArea', 'EnclosedPorch', 'GarageArea', \n",
    "                               'TotalBsmtSF',  'YearBuilt', 'SalePrice'], axis=1)\n",
    "finaltest = testdata.filter(['OverallQual','MSSubClass', 'KitchenAbvGr', \n",
    "                             'OverallCond','GrLivArea', 'EnclosedPorch', \n",
    "                             'GarageArea','TotalBsmtSF',  'YearBuilt'], axis=1)\n",
    "#finaldata.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 把'GarageArea','TotalBsmtSF'里面所有缺失值赋值为0\n",
    "finaltest.loc[finaltest.GarageArea.isnull(), 'GarageArea'] = 0\n",
    "finaltest.loc[finaltest.TotalBsmtSF.isnull(), 'TotalBsmtSF'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finaldata['SalePrice'] = np.log(finaldata['SalePrice'])\n",
    "finaldata['GrLivArea'] = np.log(finaldata['GrLivArea'])\n",
    "finaltest['GrLivArea'] = np.log(finaltest['GrLivArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total  percent\n",
       "SalePrice          0      0.0\n",
       "YearBuilt          0      0.0\n",
       "TotalBsmtSF        0      0.0\n",
       "GarageArea         0      0.0\n",
       "EnclosedPorch      0      0.0\n",
       "GrLivArea          0      0.0\n",
       "OverallCond        0      0.0\n",
       "KitchenAbvGr       0      0.0\n",
       "MSSubClass         0      0.0\n",
       "OverallQual        0      0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查数据还是否有缺失值\n",
    "total = finaldata.isnull().sum().sort_values(ascending=False)\n",
    "percent = (finaldata.isnull().sum() / finaldata.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['total', 'percent'])\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 将数据输出到CSV文件中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7.192182</td>\n",
       "      <td>0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OverallQual  MSSubClass  KitchenAbvGr  OverallCond  GrLivArea  \\\n",
       "0            5          20             1            6   6.797940   \n",
       "1            6          20             1            6   7.192182   \n",
       "\n",
       "   EnclosedPorch  GarageArea  TotalBsmtSF  YearBuilt  \n",
       "0              0       730.0        882.0       1961  \n",
       "1              0       312.0       1329.0       1958  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldata.to_csv('AmesHouseHome_FE_train.csv', index=False)\n",
    "finaltest.to_csv('AmesHouseHome_FE_test.csv', index=False)\n",
    "finaltest.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = finaldata['SalePrice'].values\n",
    "X = finaldata.drop('SalePrice', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\train\\Anaconda2\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 806.1601459 ]\n",
      " [ 837.58726774]\n",
      " [ 792.56922868]\n",
      " ..., \n",
      " [ 850.93497821]\n",
      " [ 727.63086263]\n",
      " [ 825.31926694]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.24333268, -0.04542768, -0.05351071,  0.18007195,  0.41240883,\n",
       "         0.00167453,  0.1228481 ,  0.18615278,  0.27952867]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测，下面计算score会自动调用predict\n",
    "lr_y_predict = lr.predict(X_test)\n",
    "lr_y_predict_train = lr.predict(X_train)\n",
    "#predict_outcome = lr.predict(finaltest)\n",
    "print(predict_outcome)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of LinearRegression on test is 0.902184687639\n",
      "The value of default measurement of LinearRegression on train is 0.869781361566\n"
     ]
    }
   ],
   "source": [
    "# 使用LinearRegression模型自带的评估模块（r2_score），并输出评估结果\n",
    "\n",
    "#测试集\n",
    "print 'The value of default measurement of LinearRegression on test is', lr.score(X_test, y_test)\n",
    "\n",
    "#训练集\n",
    "print 'The value of default measurement of LinearRegression on train is', lr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g7nav7wP2Bz4cEfsPapUD437gPwFL+9D2XZk5c6R8R5I+7PsIPe6fB5Zk5r7A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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1224c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - lr_y_predict_train,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44emVNAcjfyOsRospFNtVIAtdZ6ucqq0K8PPxJVx44YV8uqGJgRMvpfKIUzoV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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125bce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, lr_y_predict_train)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24355942, -0.04544611, -0.05363351,  0.18012803,  0.41208323,\n",
       "        0.00122722,  0.12281225,  0.18641428,  0.28007199])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性模型，随机梯度下降优化模型参数\n",
    "# 随机梯度下降一般在大数据集上应用，其实本项目不适合用\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "# 使用默认配置初始化线\n",
    "sgdr = SGDRegressor(max_iter=1000)\n",
    "\n",
    "# 训练：参数估计\n",
    "sgdr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "# sgdr_y_predict = sgdr.predict(X_test)\n",
    "# print(sgdr_y_predict)\n",
    "\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of SGDRegressor on test is 0.90220918595\n",
      "The value of default measurement of SGDRegressor on train is 0.869780246574\n"
     ]
    }
   ],
   "source": [
    "# 使用SGDRegressor模型自带的评估模块，并输出评估结果\n",
    "print 'The value of default measurement of SGDRegressor on test is', sgdr.score(X_test, y_test)\n",
    "print 'The value of default measurement of SGDRegressor on train is', sgdr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=[0.01, 0.1, 1, 10, 20, 40, 80, 100], cv=None,\n",
       "    fit_intercept=True, gcv_mode=None, normalize=False, scoring=None,\n",
       "    store_cv_values=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10,20, 40, 80,100]\n",
    "reg = RidgeCV(alphas=alphas, store_cv_values=True)   \n",
    "reg.fit(X_train, y_train)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SE5MspTlFtWaOY5zPlNNa7VEKSQKcDXxpL9QqSdpDvQVJVe0ELqE5LXU/8JGqWp/kyiSr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 10.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.24571478, -0.04464901, -0.0533758 ,  0.17687676,  0.40737414,\n",
       "         0.00044981,  0.12477552,  0.18596087,  0.27445981]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "plt.plot(np.log10(reg.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', reg.alpha_)\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of RidgeRegression is 0.902418734562\n"
     ]
    }
   ],
   "source": [
    "print 'The value of default measurement of RidgeRegression is', reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\train\\Anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=[0.01, 0.1, 1, 10, 100], copy_X=True, cv=None, eps=0.001,\n",
       "    fit_intercept=True, max_iter=1000, n_alphas=100, n_jobs=1,\n",
       "    normalize=False, positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10,100]\n",
    "\n",
    "lasso = LassoCV(alphas=alphas)   \n",
    "lasso.fit(X_train, y_train)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZT/YPsI/Yurc3gp/zW4TMBTbHjbud2Cz/+8RWo4Sd605ibd8BHAVe6J+L2FYk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e1470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.25119962, -0.03775   , -0.04718895,  0.16475217,  0.3998626 ,\n",
       "       -0.        ,  0.12143397,  0.18367178,  0.26431715])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "lasso.coef_  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of Lasso Regression on test is 0.903140205301\n",
      "The value of default measurement of Lasso Regression on train is 0.869254848466\n"
     ]
    }
   ],
   "source": [
    "# 使用LinearRegression模型自带的评估模块（r2_score），并输出评估结果\n",
    "print 'The value of default measurement of Lasso Regression on test is', lasso.score(X_test, y_test)\n",
    "print 'The value of default measurement of Lasso Regression on train is', lasso.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\train\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:6: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[              inf]\n",
      " [              inf]\n",
      " [              inf]\n",
      " ..., \n",
      " [              inf]\n",
      " [  3.10012105e+281]\n",
      " [              inf]]\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'Id'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-d15845e2121a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mpred_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfinaltest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Id\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"SalePrice\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;31m#pred_df.info()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mpred_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Predicting_house_price_output.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Id'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\train\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1962\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1964\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1966\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\train\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1969\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1970\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1971\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1973\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\train\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1644\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1645\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\train\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3589\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3590\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3591\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\train\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2442\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2443\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2444\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Id'"
     ],
     "output_type": "error"
    }
   ],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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